In recent years, deep learning models are achieving significant improvement in prediction problems coming from various application domains. Time series forecasting is one of the problems which is highly affected by new and powerful deep learning models. Several deep learning models have been recently applied in financial time-series analysis problems and shown performance improvements compared to traditional statistical approaches, such as the Auto-Regressive Integrated Moving Average model [1]}, [2]}, and methodologies based on shallow machine learning models like the K-Nearest Neighbor [3]} and the Support Vector Machine [4]} classifiers. While standard deep learning models, like Convolutional Neural Networks [5]}, [6]}, Recurrent Neural Networks [7]} and Long-Short Term Memory Networks [8]} have been proposed for financial time series analysis and outperformed prior machine learning solutions, the improvements in the obtained performance comes with an increased computational complexity which may render the use of such high-performing methodologies in ultra-high frequency financial time series forecasting impractical.
